module onnxrt.ops_cpu.op_dequantize_linear
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Short summary#
module mlprodict.onnxrt.ops_cpu.op_dequantize_linear
Runtime operator.
Classes#
class |
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DequantizeLinear ================ The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero … |
Properties#
property |
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Returns the list of arguments as well as the list of parameters with the default values (close to the signature). … |
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Returns the list of modified parameters. |
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Returns the list of optional arguments. |
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Returns the list of optional arguments. |
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Returns all parameters in a dictionary. |
Methods#
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Documentation#
Runtime operator.
- class mlprodict.onnxrt.ops_cpu.op_dequantize_linear.DequantizeLinear(onnx_node, desc=None, **options)#
Bases:
OpRun
The linear dequantization operator. It consumes a quantized tensor, a scale, and a zero point to compute the full precision tensor. The dequantization formula is y = (x - x_zero_point) * x_scale. ‘x_scale’ and ‘x_zero_point’ must have same shape, and can be either a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. ‘x_zero_point’ and ‘x’ must have same type. ‘x’ and ‘y’ must have same shape. In the case of dequantizing int32, there’s no zero point (zero point is supposed to be 0).
Attributes
axis: (Optional) The axis of the dequantizing dimension of the input tensor. Ignored for per-tensor quantization. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Default value is
nameaxisi1typeINT
(INT)
Inputs
Between 2 and 3 inputs.
x (heterogeneous)T: N-D quantized input tensor to be de-quantized.
x_scale (heterogeneous)tensor(float): Scale for input ‘x’. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.
x_zero_point (optional, heterogeneous)T: Zero point for input ‘x’. Shape must match x_scale. It’s optional. Zero point is 0 when it’s not specified.
Outputs
y (heterogeneous)tensor(float): N-D full precision output tensor. It has same shape as input ‘x’.
Type Constraints
T tensor(int8), tensor(uint8), tensor(int32): Constrain ‘x_zero_point’ and ‘x’ to 8-bit/32-bit integer tensor.
Version
Onnx name: DequantizeLinear
This version of the operator has been available since version 13.
Runtime implementation:
DequantizeLinear
- __init__(onnx_node, desc=None, **options)#
- _run(*args, attributes=None, verbose=0, fLOG=None)#
Should be overwritten.